Kimball University: The 10 Essential Rules of Dimensional Modeling
Follow the rules to ensure granular data, flexibility and a future-proofed information resource. Break the rules and you'll confuse users and run into data warehousing brick walls.
A student attending one of Kimball Group's recent onsite dimensional modeling classes asked me for a list of "Kimball's Commandments" for dimensional modeling. We'll refrain from using religious terminology, but let's just say the following are not-to-be-broken rules together with less stringent rule-of-thumb recommendations.
Rule #1: Load detailed atomic data into dimensional structures.
Dimensional models should be populated with bedrock atomic details to support the unpredictable filtering and grouping required by business user queries. Users typically don't need to see a single record at a time, but you can't predict the somewhat arbitrary ways they'll want to screen and roll up the details. If only summarized data is available, then you've already made assumptions about data usage patterns that will cause users to run into a brick wall when they want to dig deeper into the details. Of course, atomic details can be complemented by summary dimensional models that provide performance advantages for common queries of aggregated data, but business users cannot live on summary data alone; they need the gory details to answer their ever-changing questions.
Rule #2: Structure dimensional models around business processes.
Business processes are the activities performed by your organization; they represent measurement events, like taking an order or billing a customer. Business processes typically capture or generate unique performance metrics associated with each event. These metrics translate into facts, with each business process represented by a single atomic fact table. In addition to single process fact tables, consolidated fact tables are sometimes created that combine metrics from multiple processes into one fact table at a common level of detail. Again, consolidated fact tables are a complement to the detailed single-process fact tables, not a substitute for them.
Rule #3: Ensure that every fact table has an associated date dimension table.
The measurement events described in Rule #2 always have a date stamp of some variety associated with them, whether it's a monthly balance snapshot or a monetary transfer captured to the hundredth of a second. Every fact table should have at least one foreign key to an associated date dimension table, whose grain is a single day, with calendar attributes and nonstandard characteristics about the measurement event date, such as the fiscal month and corporate holiday indicator. Sometimes multiple date foreign keys are represented in a fact table.
The Agile ArchiveWhen it comes to managing data, donít look at backup and archiving systems as burdens and cost centers. A well-designed archive can enhance data protection and restores, ease search and e-discovery efforts, and save money by intelligently moving data from expensive primary storage systems.
2014 Analytics, BI, and Information Management SurveyITís tried for years to simplify data analytics and business intelligence efforts. Have visual analysis tools and Hadoop and NoSQL databases helped? Respondents to our 2014 InformationWeek Analytics, Business Intelligence, and Information Management Survey have a mixed outlook.
InformationWeek Tech Digest, Nov. 10, 2014Just 30% of respondents to our new survey say their companies are very or extremely effective at identifying critical data and analyzing it to make decisions, down from 42% in 2013. What gives?